When Words Sweat: Identifying Signals For Loan Default in the Text of Loan Applications
We automatically process the raw text in thousands of loan requests from an online crowdfunding platform, and find that borrowers, consciously or not, leave traces of their intentions, circumstances, and personality traits in that text. Moreover, the text is predictive of default up to three years after it was written.
Oded Netzer, Alain Lemaire, and Michal Herzenstein (2017) ,"When Words Sweat: Identifying Signals For Loan Default in the Text of Loan Applications", in NA - Advances in Consumer Research Volume 45, eds. Ayelet Gneezy, Vladas Griskevicius, and Patti Williams, Duluth, MN : Association for Consumer Research, Pages: 53-56.
Oded Netzer, Columbia University, USA
Alain Lemaire, Columbia University, USA
Michal Herzenstein, University of Delaware, USA
NA - Advances in Consumer Research Volume 45 | 2017
Trusting the data, the self and “the other” in self tracking practices
Dorthe Brogård Kristensen, University of Southern Denmark, Denmark
F9. Protection against Deception: The Moderating Effects of Knowledge Calibration on Consumer Responses to Ambiguous Advertisement Information
Joel Alan Mohr, Queens University, Canada
Peter A. Dacin, Queens University, Canada
Ecce Machina Humana: Examining Competence and Warmth in Consumer Robots The two fundamental social judgment dimensions-competence and warmth-are as relevant for judging consumer robots as for humans. We find that competence has an increasing positive eff